Vehicle control method, vehicle control device, and storage medium

ABSTRACT

A vehicle control method includes recognizing an object, generating plurality of target trajectories which the vehicle travels using a plurality of models, and automatically controlling driving of the vehicle based on the target trajectory, the plurality of models include a first model which is a rule based model or a model based model, and a second model which is a machine learning based model, and the second target trajectory is selected, when the vehicle accelerates after a speed of the vehicle has been equal to or smaller than a predetermined value, under a condition in which driving of the vehicle is controlled based on a first target trajectory output by the first model and under a condition in which driving of the vehicle is controlled based on a second target trajectory output by the second model.

CROSS-REFERENCE TO RELATED APPLICATION

Priority is claimed on Japanese Patent Application No. 2020-063096,filed Mar. 31, 2020, the content of which is incorporated herein byreference.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to a vehicle control method, a vehiclecontrol device, and a storage medium.

Description of Related Art

A technology of generating a target trajectory along which a vehicle isto travel in the future is known (for example, Japanese UnexaminedPatent Application, First Publication No. 2019-108124).

SUMMARY OF THE INVENTION

However, in the related art, in some cases, target trajectories that donot match the surrounding conditions may be generated. As a result,driving of the vehicle may not be smoothly controlled.

An aspect of the present invention is directed to providing a vehiclecontrol method, a vehicle control device, and a storage medium that arecapable of smoothly controlling driving of a vehicle.

A vehicle control method, a vehicle control device, and a storage mediumaccording to the present invention employ the following configurations.

A first aspect of the present invention is a vehicle control methodincluding: recognizing an object present around the vehicle; calculatinga region of a risk distributed around the recognized object; inputtingthe calculated region to each of a plurality of models that output atarget trajectory along which the vehicle is to travel when the regionis input, and generating a plurality of target trajectories on the basisof an output result of each of the plurality of models to which theregion has been input; and automatically controlling driving of thevehicle on the basis of the generated target trajectory, wherein theplurality of models include a first model which is a rule based model ora model based model that outputs the target trajectory from the region,and a second model which is a machine learning based model learned tooutput the target trajectory when the region is input, and the secondtarget trajectory is selected from the first target trajectory and thesecond target trajectory in a case the vehicle accelerates after a speedof the vehicle has been equal to or smaller than a predetermined valueunder a first condition and in a case the vehicle accelerates after aspeed of the vehicle has been equal to or smaller than the predeterminedvalue under a second condition, the first condition being a condition inwhich driving of the vehicle is controlled based on a first targettrajectory that is the target trajectory output by the first model, thesecond condition being a condition in which driving of the vehicle iscontrolled based on a second target trajectory that is the targettrajectory output by the second model.

According to a second aspect, in the first aspect, further includes:predicting a future trajectory of the recognized object, and in a casethe future trajectory of the object crosses the target trajectory of thevehicle, a determination is made that the vehicle will accelerate aftera speed of the vehicle has been equal to or smaller than thepredetermined value under the first condition and that the vehicle willaccelerate after a speed of the vehicle has been equal to or smallerthan the predetermined value under the second condition.

According to a third aspect, in the first or second aspect, thepredetermined value is zero, and the second target trajectory isselected among the first target trajectory and the second targettrajectory in a case an acceleration of the vehicle is within apredetermined range after a speed of the vehicle has been stopped underthe first condition and an acceleration of the vehicle is within apredetermined range after a speed of the vehicle has been stopped underthe second condition.

A fourth aspect is a vehicle control device including: a recognitionpart configured to recognize an object present around a vehicle; acalculating part configured to calculate a region of a risk distributedaround the object recognized by the recognition part; a generating partconfigured to input the region calculated by the calculating part toeach of a plurality of models that output a target trajectory alongwhich the vehicle is to travel when the region is input, and generate aplurality of target trajectories on the basis of an output result ofeach of the plurality of models to which the region has been input; anda driving controller configured to automatically control driving of thevehicle on the basis of the target trajectory generated by thegenerating part, wherein the plurality of models include a first modelwhich is rule-based model or model-based model that outputs the targettrajectory from the region, and a second model which is a machinelearning based model learned to output the target trajectory when theregion is input, and the generating part selects a second targettrajectory among a first target trajectory and a second targettrajectory in a case the vehicle accelerates after a speed of thevehicle has been equal to or smaller than a predetermined value under afirst condition and in a case the vehicle accelerates after a speed ofthe vehicle has been equal to or smaller than the predetermined valueunder a second condition, the first condition being a condition in whichdriving of the vehicle is controlled based on a first target trajectorythat is the target trajectory output by the first model, the secondcondition being a condition in which driving of the vehicle iscontrolled based on a second target trajectory that is the targettrajectory output by the second model.

A fifth aspect is a computer-readable storage medium on which a programis stored to execute a computer mounted on a vehicle to: recognize anobject present around the vehicle; calculate a region of a riskdistributed around the recognized object; input the calculated region toeach of a plurality of models that output a target trajectory alongwhich the vehicle is to travel when the region is input, and generate aplurality of target trajectories on the basis of an output result ofeach of the plurality of models to which the region has been input; andautomatically control driving of the vehicle on the basis of thegenerated target trajectory, wherein the plurality of models include afirst model which is rule-based model or model-based model that outputsthe target trajectory from the region, and a second model which is amachine learning based model learned to output the target trajectorywhen the region is input, and the generating part selects a secondtarget trajectory among a first target trajectory and a second targettrajectory in a case the vehicle accelerates after a speed of thevehicle has been equal to or smaller than a predetermined value under afirst condition and in a case the vehicle accelerates after a speed ofthe vehicle has been equal to or smaller than the predetermined valueunder a second condition, the first condition being a condition in whichdriving of the vehicle is controlled based on a first target trajectorythat is the target trajectory output by the first model, the secondcondition being a condition in which driving of the vehicle iscontrolled based on a second target trajectory that is the targettrajectory output by the second model.

According to any one of the above-mentioned aspects, it is possible tosmoothly control driving of a vehicle by generating a target trajectoryappropriate for surrounding circumstances.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a configuration view of a vehicle system using a vehiclecontrol device according to an embodiment.

FIG. 2 is a functional configuration view of a first controller, asecond controller, and a storage according to the embodiment.

FIG. 3 is a view for describing a risk region.

FIG. 4 is a view showing a variation in risk potential in a Y directionat a certain coordinate x1.

FIG. 5 is a view showing a variation in risk potential in a Y directionat a certain coordinate x2.

FIG. 6 is a view showing a variation in risk potential in a Y directionat a certain coordinate x3.

FIG. 7 is a view showing a variation in risk potential in an X directionat a certain coordinate x4.

FIG. 8 is a view showing a risk region determined by a risk potential.

FIG. 9 is a view schematically showing a generating method of a targettrajectory.

FIG. 10 is a view showing an example of a target trajectory output froma model.

FIG. 11 is a flowchart showing an example of a series of processingflows by an automatic driving control device according to theembodiment.

FIG. 12 is a view showing an example of a situation that a host vehiclecan encounter.

FIG. 13 is a view showing an example of a situation in which driving ofa host vehicle is controlled on the basis of the target trajectory.

FIG. 14 is a view showing an example of a hardware configuration of anautomatic driving control device of the embodiment.

DETAILED DESCRIPTION OF THE INVENTION

Hereinafter, embodiments of a vehicle control device, a vehicle controlmethod, and a program of the present invention will be described withreference to the accompanying drawings. The vehicle control device ofthe embodiment is applied to, for example, an automatic travelingvehicle. Automatic traveling is, for example, controlling driving of thevehicle by controlling one or both of a speed or steering of thevehicle. The above-mentioned driving control of the vehicle includesvarious types of driving control, for example, an adaptive cruisecontrol system (ACC), a traffic jam pilot (TJP), auto lane changing(ALC), a collision mitigation brake system (CMBS) or a lane keepingassistance system (LKAS). Driving of the automatic traveling vehicle maybe controlled by manual driving of an occupant (a driver).

[Entire Configuration]

FIG. 1 is a configuration view of a vehicle system 1 using a vehiclecontrol device according to the embodiment. A vehicle on which thevehicle system 1 is mounted (hereinafter, referred to as a host vehicleM) is, for example, a two-wheeled, three-wheeled, or four-wheeledvehicle, and a driving source thereof is an internal combustion enginesuch as a diesel engine, a gasoline engine, or the like, an electricmotor, or a combination of these. The electric motor is operated usingan output generated by a generator connected to the internal combustionengine, or discharged energy of a secondary battery or a fuel cell.

The vehicle system 1 includes, for example, a camera 10, a radar device12, light detection and ranging (LIDAR) 14, an object recognition device16, a communication device 20, a human machine interface (HMI) 30, avehicle sensor 40, a navigation device 50, a map positioning unit (MPU)60, a driving operator 80, an automatic driving control device 100, atraveling driving power output device 200, a brake device 210, and asteering device 220. These devices or instruments are connected to eachother by a multiple communication line such as a controller area network(CAN) communication line or the like, a serial communication line, awireless communication network, or the like. The configuration shown inFIG. 1 is merely an example, a part of the configuration may be omitted,and another configuration may be added. The automatic driving controldevice 100 is an example of “a vehicle control device.”

The camera 10 is, for example, a digital camera using a solid-stateimage sensing device such as a charge coupled device (CCD), acomplementary metal oxide semiconductor (CMOS), or the like. The camera10 is attached to an arbitrary place of the host vehicle M. For example,when a side in front of the host vehicle M is imaged, the camera 10 isattached to an upper section of a front windshield, a rear surface of arearview mirror, or the like. In addition, when a side behind the hostvehicle M is imaged, the camera 10 is attached to an upper section of arear windshield, or the like. In addition, when the right side or theleft side of the host vehicle M is imaged, the camera 10 is attached toa right side surface or a left side surface of a vehicle body or a doormirror. The camera 10 images surroundings of the host vehicle M, forexample, periodically and repeatedly. The camera 10 may be a stereocamera.

The radar device 12 radiates radio waves such as millimeter waves or thelike to surroundings of the host vehicle M, and simultaneously, detectsthe radio waves (reflected waves) reflected by the object to detect aposition (a distance and an azimuth) of at least the object. The radardevice 12 is attached to an arbitrary place of the host vehicle M. Theradar device 12 may detect a position and a speed of the object using afrequency modulated continuous wave (FM-CW) method.

The LIDAR 14 radiates light to surroundings of the host vehicle M, andmeasures the scattered light of the radiated light. The LIDAR 14 detectsa distance to a target on the basis of a time from emission to receptionof light. The radiate light is, for example, a pulse-shaped laser beam.The LIDAR 14 is attached to an arbitrary place of the host vehicle M.

The object recognition device 16 recognizes a position, a type, a speed,or the like, of the object by performing sensor fusion processing withrespect to the detection result by some or all of the camera 10, theradar device 12, and the LIDAR 14. The object recognition device 16outputs the recognized result to the automatic driving control device100. In addition, the object recognition device 16 may output thedetection results of the camera 10, the radar device 12 and the LIDAR 14directly to the automatic driving control device 100. In this case, theobject recognition device 16 may be omitted from the vehicle system 1.

The communication device 20 uses, for example, a cellular network, aWi-Fi network, a Bluetooth (registered trademark), dedicated short rangecommunication (DSRC), or the like, comes in communication with anothervehicle present in the vicinity of the host vehicle M, or comes incommunication with various server devices via a radio base station.

The HMI 30 receives an input operation by an occupant in the hostvehicle M while providing various types of information to the occupant(including a driver). The HMI 30 includes, for example, a display, aspeaker, a buzzer, a touch panel, a microphone, a switch, a key, or thelike.

The vehicle sensor 40 includes a vehicle speed sensor configured todetect a speed of the host vehicle M, an acceleration sensor configuredto detect an acceleration, a yaw rate sensor configured to detect anangular speed around a vertical axis, and an azimuth sensor configuredto detect an orientation of the host vehicle M.

The navigation device 50 includes, for example, a global navigationsatellite system (GNSS) receiver 51, a navigation HMI 52, and a routedetermining part 53. The navigation device 50 holds first mapinformation 54 in a storage device such as a hard disk drive (HDD) aflash memory, or the like.

The GNSS receiver 51 specifies a position of the host vehicle M on thebasis of a signal received from the GNSS satellite. The position of thehost vehicle M may be specified or complemented by an inertialnavigation system (INS) that uses output of the vehicle sensor 40.

The navigation HMI 52 includes a display device, a speaker, a touchpanel, a key, and the like. The navigation HMI 52 may be partially orentirely shared with the HMI 30 described above. For example, theoccupant may input a destination of the host vehicle M to the navigationHMI 52 instead of (or in addition to) inputting the destination of thehost vehicle M to the HMI 30.

The route determining part 53 determines, for example, a route(hereinafter, a route on a map) from a position of the host vehicle Mspecified by the GNSS receiver 51 (or an input arbitrary position) to adestination input by an occupant using the HMI 30 or the navigation HMI52 with reference to the first map information 54.

The first map information 54 is, for example, information in which aroad shape is expressed by a link showing a road and a node connected bythe link. The first map information 54 may include a curvature of aroad, point of interest (POI) information, or the like. The route on amap is output to the MPU 60.

The navigation device 50 may perform route guidance using the navigationHMI 52 on the basis of the route on a map. The navigation device 50 maybe realized by, for example, a function of a terminal device such as asmart phone, a tablet terminal, or the like, held by the occupant. Thenavigation device 50 may transmit the current position and thedestination to the navigation server via the communication device 20,and acquire the same route as the route on a map from the navigationserver.

The MPU 60 includes, for example, a recommended lane determining part61, and holds second map information 62 in a storage device such as anHDD, a flash memory, or the like. The recommended lane determining part61 divides the route on a map provided from the navigation device 50into a plurality of blocks (for example, divided at each 100 [m] in anadvance direction of the vehicle), and determines a recommended lane ateach block with reference to the second map information 62. Therecommended lane determining part 61 performs determination of whichlane the vehicle travels from the left. The recommended lane determiningpart 61 determines a recommended lane such that the host vehicle M cantravel a reasonable route so as to reach a branch destination when adiverging place is present on the route on a map.

The second map information 62 is map information that has a higherprecision than the first map information 54. The second map information62 includes, for example, information of a center of a lane, informationof a boundary of a lane, or the like. In addition, the second mapinformation 62 may include road information, traffic regulationinformation, address information (address/zip code), facilityinformation, telephone number information, and the like. The second mapinformation 62 may be updated at any time by bringing the communicationdevice 20 in communication with another device.

The driving operator 80 includes, for example, an acceleration pedal, abrake pedal, a shift lever, a steering wheel, a modified steer, ajoystick, and other operators. A sensor configured to detect anoperation amount or existence of an operation is attached to the drivingoperator 80, and the detection result is output to some or all of theautomatic driving control device 100, the traveling driving power outputdevice 200, the brake device 210, and the steering device 220.

The automatic driving control device 100 includes, for example, a firstcontroller 120, a second controller 160, and a storage 180. The firstcontroller 120 and the second controller 160 are realized by executing aprogram (software) using a hardware processor such as a centralprocessing unit (CPU), a graphics processing unit (GPU) or the like.Some or all of these components may be realized by hardware (a circuitpart; including a circuitry) such as large scale integration (LSI), anapplication specific integrated circuit (ASIC), a field-programmablegate array (FPGA), or the like, or may be realized by cooperation ofsoftware and hardware. The program may be previously stored in a storagedevice (a storage device including a non-transient storage medium) suchas an HDD, a flash memory, or the like, of the automatic driving controldevice 100, stored in a detachable storage medium such as a DVD, aCD-ROM, or the like, or installed on an HDD or a flash memory of theautomatic driving control device 100 by mounting a storage medium on adrive device (a non-transient storage medium).

The storage 180 is realized by the above-mentioned various types ofstorage devices. The storage 180 is realized by, for example, an HDD, aflash memory, an electrically erasable programmable read only memory(EEPROM), a read only memory (ROM), a random access memory (RAM), or thelike. Rule based model data 182 or deep neural network (DNN(s)) modeldata 184, or the like, is stored in the storage 180, for example, inaddition to the program read and executed by the processor. The rulebased model data 182 or the DNN model data 184 will be described belowin detail.

FIG. 2 is a functional configuration view of the first controller 120,the second controller 160 and the storage 180 according to theembodiment. The first controller 120 includes, for example, arecognition part 130 and an action plan generating part 140.

The first controller 120 realizes, for example, both of a function ofartificial intelligence (AI) and a function of a previously providedmodel at the same time. For example, a function of “recognizing acrossroad” is executed parallel to recognition of a crossroad throughdeep learning or the like and recognition based on a previously providedcondition (a signal that enables matching of patterns, road markings, orthe like), and may be realized by scoring and comprehensively evaluatingthem. Accordingly, reliability of automatic driving is guaranteed.

The recognition part 130 recognizes a situation or an environment ofsurroundings of the host vehicle M. For example, the recognition part130 recognizes an object present around the host vehicle M on the basisof information input from the camera 10, the radar device 12, and theLIDAR 14 via the object recognition device 16. The object recognized bythe recognition part 130 includes, for example, a bicycle, a motorcycle,a four-wheeled automobile, a pedestrian, road signs, road markings, roadmarking lines, electric poles, a guardrail, falling objects, or thelike. In addition, the recognition part 130 recognizes a state such as aposition, a speed, an acceleration, or the like, of the object. Theposition of the object is recognized as a position on relativecoordinates (i.e., a relative position with respect to the host vehicleM) using, for example, a representative point (a center of gravity, adriving axial center, or the like) of the host vehicle M as an origin,and is used for control. The position of the object may be displayed ata representative point such as a center of gravity, a corner, or thelike, of the object, or may be displayed as an expressed region. The“state” of the object may include an acceleration, a jerk, or “an actionstate” (for example, whether a lane change is performed or to beperformed) of the object.

In addition, the recognition part 130 recognizes, for example, a lane inwhich the host vehicle M is traveling (hereinafter, a host lane), aneighboring lane adjacent to the host lane, or the like. For example,the recognition part 130 recognizes a space between road marking linesas a host lane or a neighboring lane by comparing a pattern (forexample, arrangement of solid lines and broken lines) of road markinglines obtained from the second map information 62 with a pattern of roadmarking lines around the host vehicle M recognized from an imagecaptured by the camera 10.

In addition, the recognition part 130 may recognize a lane such as ahost lane or a neighboring lane by recognizing traveling lane boundaries(road boundaries) including road marking lines, road shoulders,curbstones, median strips, guardrails, and the like, while not beinglimited to road marking lines. In the recognition, the position of thehost vehicle M acquired from the navigation device 50 or a processingresult by the INS may be added. In addition, the recognition part 130may recognize a temporary stop line, an obstacle, a red signal, atollgate, and other road events.

The recognition part 130 recognizes a relative position or an attitudeof the host vehicle M with respect to the host lane when the host laneis recognized. The recognition part 130 may recognize a separation froma lane center of a reference point of the host vehicle M and an angle ofthe host vehicle M with respect to a line that connects a lane center inan advance direction as a relative position and an attitude of the hostvehicle M with respect to the host lane. Instead of this, therecognition part 130 may recognize a position or the like of a referencepoint of the host vehicle M with respect to any side end portion (a roadmarking line or a road boundary) of the host lane as a relative positionof the host vehicle M with respect to the host lane.

The action plan generating part 140 includes, for example, an eventdetermining part 142, a risk region calculating part 144 and a targettrajectory generating part 146.

The event determining part 142 determines a traveling aspect ofautomatic traveling when the host vehicle M is automatically travelingon a route in which a recommended lane is determined. Hereinafter,information that defines a traveling aspect of the automatic travelingwill be described while being referred to as an event.

The event includes, for example, a fixed speed traveling event, afollowing traveling event, a lane change event, a diverging event, amerging event, a take-over event, or the like. The fixed speed travelingevent is a traveling aspect of causing the host vehicle M to travelalong the same lane at a fixed speed. The following traveling event is atraveling aspect of causing the host vehicle M to follow another vehiclepresent within a predetermined distance in front of the host vehicle Mon the host lane (for example, within 100 [m]) and closest to the hostvehicle M (hereinafter, referred to as a preceding vehicle).

“Following” may be, for example, a traveling aspect of maintaining aconstant inter-vehicle distance (a relative distance) between the hostvehicle M and a preceding vehicle, or may be a traveling aspect ofcausing the host vehicle M to travel along a center of the host lane, inaddition to maintaining the constant inter-vehicle distance between thehost vehicle M and the preceding vehicle.

The lane change event is a traveling aspect of causing the host vehicleM to change lane from the host lane to a neighboring lane. The divergingevent is a traveling aspect of causing the host vehicle M to move to alane on the side of the destination at a diverging point of the road.The merging event is a traveling aspect of causing the host vehicle M tojoin a main line at a merging point. The take-over event is a travelingaspect of terminating automatic traveling and switching automaticdriving to manual driving.

In addition, the event may include, for example, an overtaking event, anavoiding event, or the like. The overtaking event is a traveling aspectof causing the host vehicle M to change lane to a neighboring lanetemporarily, overtake the preceding vehicle in the neighboring lane andchange lane to the original lane again. The avoiding event is atraveling aspect of causing the host vehicle M to perform at least oneof braking and steering to avoid an obstacle present in front of thehost vehicle M.

In addition, for example, the event determining part 142 may change theevent already determined with respect to the current section to anotherevent, or determine a new event with respect to the current sectionaccording to a situation of the surroundings recognized by therecognition part 130 when the host vehicle M is traveling.

The risk region calculating part 144 calculates a risk regionpotentially distributed or present around the object recognized by therecognition part 130 (hereinafter, referred to as a risk region RA). Therisk is, for example, a risk that an object exerts an influence on thehost vehicle M. More specifically, the risk may be a risk of the hostvehicle M being caused to brake suddenly because a preceding vehicle hassuddenly slowed down or another vehicle has cut in front of the hostvehicle M from a neighboring lane, or may be a risk of the host vehicleM being forced to be steered suddenly because a pedestrian or a bicyclehas entered the roadway. In addition, the risk may be a risk that thehost vehicle M will exert an influence on the object. Hereinafter, thelevel of such risk is treated as a quantitative index value, and theindex value which will be described below is referred to as “a riskpotential p.”

FIG. 3 is a view for explaining the risk region RA. LN1 in the drawingdesignates a road marking line that partitions off the host lane on oneside, and LN2 designates a road marking line that partitions off thehost lane on the other side and a road marking line that partitions offa neighboring lane on one side. LN3 designates the other road markingline that divides the neighboring lane. In the plurality of road markinglines, LN1 and LN3 designate roadway edge markings, and LN2 designates acenter line that vehicles are allowed to pass beyond when overtaking. Inaddition, in the example shown, a preceding vehicle m1 is present infront of the host vehicle M on the host lane. X in the drawingsdesignates a direction in which the vehicle is advancing, Y designates awidthwise direction of the vehicle, and Z designates a verticaldirection.

In the case of the situation shown, in the risk region RA, the riskregion calculating part 144 increases the risk potential p in a regionclose to the roadway edge markings LN1 and LN3, and decreases the riskpotential p in a region distant from the roadway edge markings LN1 andLN3.

In addition, in the risk region RA, the risk region calculating part 144increases the risk potential p as it approaches the region close to thecenter line LN2 and decreases the risk potential p as it approaches theregion far from the center line LN2. Since the center line LN2 isdifferent from the roadway edge markings LN1 and LN3 and the vehicle isallowed to deviate from the center line LN2, the risk region calculatingpart 144 causes the risk potential p with respect to the center line LN2to be lower than the risk potential p with respect to the roadway edgemarkings LN1 and LN3.

In addition, in the risk region RA, the risk region calculating part 144increases the risk potential p as it approaches a region close to thepreceding vehicle m1 that is one of an object, and decreases the riskpotential p as it approaches a region far from the preceding vehicle m1.That is, in the risk region RA, the risk region calculating part 144 mayincrease the risk potential p as a relative distance between the hostvehicle M and the preceding vehicle m1 becomes shorter, and may decreasethe risk potential p as the relative distance between the host vehicle Mand the preceding vehicle m1 becomes longer. Here, the risk regioncalculating part 144 may increase the risk potential p as an absolutespeed or an absolute acceleration of the preceding vehicle m1 isincreased. In addition, the risk potential p may be appropriatelydetermined according to a relative speed or a relative accelerationbetween the host vehicle M and the preceding vehicle m1, a time tocollision (TTC), or the like, instead of or in addition to the absolutespeed or the absolute acceleration of the preceding vehicle m1.

FIG. 4 is a view showing variation in the risk potential p in the Ydirection at a certain coordinate x1. Here, y1 in the drawing designatesa position (coordinate) of the roadway edge marking LN1 in the Ydirection, y2 designates a position (coordinate) of the center line LN2in the Y direction, and y3 designates a position (coordinate) of theroadway edge marking LN3 in the Y direction.

As shown, the risk potential p is highest in the vicinity of thecoordinates (x1, y1) at which the roadway edge marking LN1 is present orin the vicinity of the coordinates (x1, y3) at which the roadway edgemarking LN3 is present, and the risk potential p is the second highestin the vicinity of the coordinates (x1, y2) at which the center line LN2is present after the coordinates (x1, y1) or (x1, y3). As describedabove, in a region in which the risk potential p is equal to or greaterthan a previously determined threshold Th, in order to prevent thevehicle from entering the region, the target trajectory TR is notgenerated.

FIG. 5 is a view showing variation in the risk potential p in the Ydirection at a certain coordinate x2. The coordinate x2 is closer to thepreceding vehicle m1 than the coordinate x1 is. For this reason,although the preceding vehicle m1 is not present in the region betweenthe coordinates (x2, y1) at which the roadway edge marking LN1 ispresent and the coordinates (x2, y2) at which the center line LN2 ispresent, a risk can be considered such as the sudden deceleration of thepreceding vehicle m1 or the like. As a result, the risk potential p inthe region between (x2, y1) and (x2, y2) tends to be higher than therisk potential p in the region between (x1, y1) and (x1, y2), forexample, the threshold Th or more.

FIG. 6 is a view showing a variation in the risk potential p in the Ydirection at a certain coordinate x3. The preceding vehicle m1 ispresent at the coordinate x3. For this reason, the risk potential p inthe region between the coordinates (x3, y1) at which the roadway edgemarking LN1 is present and the coordinates (x3, y2) at which the centerline LN2 is present is higher than the risk potential p in the regionbetween (x2, y1) and (x2, y2), and is equal to or greater than thethreshold Th.

FIG. 7 is a view showing a variation in the risk potential p in the Xdirection at a certain coordinate y4. The coordinate y4 are intermediatecoordinates between y1 and y2, and the preceding vehicle m1 is presentat the coordinate y4. For this reason, the risk potential p is highestat the coordinates (x3, y4), the risk potential p at the coordinates(x2, y4) farther from the preceding vehicle m1 than the coordinates (x3,y4) is lower than the risk potential p at the coordinates (x3, y4), andthe risk potential p at the coordinates (x1, y4) farther from thepreceding vehicle m1 than the coordinates (x2, y4) is lower than therisk potential p at the coordinates (x2, y4).

FIG. 8 is a view showing the risk region RA determined by the riskpotential p. As shown, in the risk region calculating part 144, the riskregion RA is divided into a plurality of mesh squares (also referred toas grid squares), and the risk potential p is associated with each ofthe plurality of mesh squares. For example, the risk potential p_(ij)corresponds to the mesh square (x_(i), y_(j)). That is, the risk regionRA is expressed by a data structure referred to as a vector or a tensor.

The risk region calculating part 144 normalizes the risk potential p ofeach mesh when the risk potential p corresponds to each of the pluralityof meshes.

For example, the risk region calculating part 144 may normalize the riskpotential p such that the maximum value of the risk potential p is 1 andthe minimum value is 0. Specifically, the risk region calculating part144 selects the risk potential p_(max) that becomes the maximum valueand the risk potential p_(min) that becomes the minimum value among therisk potential p of all of the meshes included in the risk region RA.The risk region calculating part 144 selects one mesh (x_(i), y_(i)) ofinterest from all of the meshes included in the risk region RA,subtracts the minimum risk potential p_(min) from the risk potentialp_(ij) corresponding to the mesh (x_(i), y_(j)), subtracts the minimumrisk potential p_(min) from the maximum risk potential p_(max), anddivides (p_(ij)−p_(min)) by (p_(max)−p_(min)). The risk regioncalculating part 144 repeats the above-mentioned processing whilechanging the mesh of interest. Accordingly, the risk region RA isnormalized such that the maximum value of the risk potential p is 1 andthe minimum value is 0.

In addition, the risk region calculating part 144 may calculate anaverage value μ and a standard deviation σ of the risk potential p ofall the meshes included in the risk region RA, subtract the averagevalue μ from the risk potential p_(ij) corresponding to the mesh (x_(i),y_(j)), and divide (p_(ij)−μ) by the standard deviation σ. Accordingly,the risk region RA is normalized such that the maximum value of the riskpotential p is 1 and the minimum value is 0.

In addition, the risk region calculating part 144 may normalize the riskpotential p such that the maximum value of the risk potential p is anarbitrary M and the minimum value is an arbitrary m. Specifically, when(p_(ij)−p_(min))/(p_(max)−p_(min)) is A, the risk region calculatingpart 144 multiplies A by (M−m), and adds m to A (M−m). Accordingly, therisk region RA is normalized such that the maximum value of the riskpotential p becomes M and the minimum value becomes m.

Returning to the description in FIG. 2, the target trajectory generatingpart 146 generates the future target trajectory TR to allow the hostvehicle M to automatically travel (independently of an operation of thedriver) in the traveling aspect defined by the event, such that the hostvehicle M is able to travel in the recommended lane determined by therecommended lane determining part 61, and further, the host vehicle M isable to respond to the surrounding situation when traveling in therecommended lane. The target trajectory TR includes, for example, aposition element that determines the position of the host vehicle M inthe future, and a speed element that has determined the speed or thelike of the host vehicle M in the future.

For example, the target trajectory generating part 146 determines aplurality of points (trajectory points) at which the host vehicle Mshould arrive in sequence as position elements of the target trajectoryTR. The trajectory point is a point at which the host vehicle M shouldarrive after each of predetermined traveling distances (for example,about every several [m]). The predetermined traveling distance may becalculated, for example, according to a distance along a road whentraveling on a route.

In addition, the target trajectory generating part 146 determines atarget speed v and a target acceleration α for every predeterminedsampling time (for example, about every several fractions of a [sec]) asa speed element of the target trajectory TR. In addition, the trajectorypoint may be a position at which the host vehicle M is to arrive in asampling time for every predetermined sampling time. In this case, thetarget speed v or the target acceleration α is determined according toan interval of the sampling times and the trajectory points.

For example, the target trajectory generating part 146 reads the rulebased model data 182, the DNN model data 184, or the like, from thestorage 180, and generates the target trajectory TR using a modeldefined by the read data.

The rule based model data 182 is information (a program or a datastructure) that defines one or a plurality of rule based models MDL1.The rule based model MDL1 is a model of deriving the target trajectoryTR from the risk region RA on the basis of a rule group previouslydetermined by an expert or the like. Such a rule based model MDL1 isalso referred to as an expert system because the rule group isdetermined by an expert or the like. The rule group includes a law suchas a road traffic law or the like, regulations, practices, or the like.The rule based models MDL1 is an example of “a first model.”

For example, in the rule group, a rule to which a target trajectory TRxis uniquely associated in a certain condition X may exist. The conditionX is, for example, the risk region RA input to the rule based modelsMDL1 is same as the risk region RA_(X) that can be generated when thehost vehicle M is traveling on a road having one lane on each side and apreceding vehicle with a speed of XX [km/h] is present within apredetermined distance in front of the host vehicle M. The targettrajectory TRx is, for example, the target trajectory TR in which atarget speed is v_(X), a target acceleration is α_(X), a displacementamount of steering is u_(X), and a curvature of the trajectory is κ_(X).According to such a rule, the rule based models MDL1 output the targettrajectory TRx when the risk region RA that satisfies the condition X isinput.

Although experts and others determine the rule group, it is rare thatall kinds of rules are comprehensively determined. For this reason, itis also assumed that the host vehicle M is not present in the rule group(a circumstance not expected by the experts), and in some cases, therisk region RA that does not correspond to the rule group is entered tothe rule based model MDL1. In this case, the rule based model MDL1 doesnot output the target trajectory TR. Instead of this, when the riskregion RA that does not correspond to the rule group is input, the rulebased model MDL1 may output the previously determined target trajectoryTR that does not depend on the risk region RA in a current status suchas traveling the current lane at a previously determined speed. That is,when the risk region RA that is not expected in advance is input, therule group may include a general rule corresponding to an irregularcircumstance such as outputting the previously determined targettrajectory TR that does not depend on the risk region RA in the currentstatus.

The DNN model data 184 is information (a program or a data structure)that define one or a plurality of DNN models MDL2. The DNN model MDL2 isa deep learning model learned to output the target trajectory TR whenthe risk region RA is input. Specifically, the DNN model MDL2 may be aconvolutional neural network (CNN), a reccurent neural network (RNN), ora combination of these. The DNN model data 184 includes, for example,various types of information such as coupling information showing howunits included in a plurality of layers constituting the neural networkare coupled to each other, a coupling coefficient applied to the datainput and output between the coupled units, or the like. The DNN modelMDL2 is an example of “a second model.”

The coupling information includes, for example, information thatdesignates the number of units included in each layer, a type of a unitwhich is a coupling mate member of each of the units, or the like, andinformation of an activation function of each unit, a gate providedbetween the units of the hidden layer, or the like. The activationfunction may be, for example, a normalized linear function (an ReLUfunction), or may be a Sigmoid function, a step function, or the otherfunctions. The gate selectively passes or weights the data transmittedbetween the units, for example, according to a value (for example, 1 or0) returned by the activation function. The coupling coefficientincludes, for example, a weight coefficient given to the output datawhen the data is output from a unit of a certain layer to a unit of adeeper layer in the hidden layer of the neural network. In addition, thecoupling coefficient may include a bias element peculiar to each layer.

The DNN model MDL2 is sufficiently learned on the basis of, for example,instructor data. The instructor data is, for example, a data set inwhich the target trajectory TR, which is a correct answer that the DNNmodel MDL2 should output, is associated with the risk region RA as aninstructor label (also referred to as a target). That is, the instructordata is a data set in which the risk region RA that is input data andthe target trajectory TR that is output data are combined. The targettrajectory TR, which is the correct answer, may be, for example, thetarget trajectory that passes the mesh having the lowest risk potentialp, which is less than the threshold Th, among the plurality of meshescontained in the risk region RA. In addition, the target trajectory TR,which is the correct answer, may be, for example, an actual trajectoryof a vehicle driven by a driver in a certain risk region RA.

The target trajectory generating part 146 inputs the risk region RAcalculated by the risk region calculating part 144 to each of the rulebased models MDL1 and the DNN models MDL2, and generates the targettrajectory TR on the basis of the output result of each of the modelsMDL to which the risk region RA is input.

FIG. 9 is a view schematically showing a generating method of the targettrajectory TR. For example, the target trajectory generating part 146inputs a vector or a tensor representing the risk region RA to the DNNmodel MDL2 when the certain DNN model MDL2 is selected. In the exampleshown, the risk region RA is represented as a second-order tensor with mrows and n columns. The model MDL to which the vector or the tensorrepresenting the risk region RA is input outputs the target trajectoryTR. The target trajectory TR is represented by, for example, the vectoror the tensor including a plurality of elements such as a target speedv, a target acceleration α, a displacement amount u of steering, and acurvature κ of a trajectory.

FIG. 10 is a view showing an example of the target trajectory TR outputby the model MDL. Like the example shown, since the risk potential paround the preceding vehicle m1 is increased, the target trajectory TRis generated to avoid it. As a result, the host vehicle M changes thelane to a neighboring lane partitioned by road marking lines LN2 andLN3, and overtakes the preceding vehicle m1.

Returning to the description of FIG. 2, the second controller 160controls the traveling driving power output device 200, the brake device210, and the steering device 220 such that the host vehicle M passesthrough the target trajectory TR generated by the target trajectorygenerating part 146 on time. The second controller 160 includes, forexample, a first acquisition part 162, a speed controller 164 and asteering controller 166. The second controller 160 is an example of “adriving controller.”

The first acquisition part 162 acquires the target trajectory TR fromthe target trajectory generating part 146, and stores the acquiredtarget trajectory TR in a memory of the storage 180.

The speed controller 164 controls one or both of the traveling drivingpower output device 200 and the brake device 210 on the basis of thespeed element (for example, the target speed v, the target accelerationα, or the like) included in the target trajectory TR stored in thememory.

The steering controller 166 controls the steering device 220 accordingto the position element included in the target trajectory stored in thememory (for example, a curvature x of the target trajectory, adisplacement amount u of the steering according to the position of thetrajectory point, or the like).

Processing of the speed controller 164 and the steering controller 166is realized by, for example, a combination of feedforward control andfeedback control. As an example, the steering controller 166 combinesand executes the feedforward control according to the curvature of theroad in front of the host vehicle M and the feedback control on thebasis of the separation from the target trajectory TR.

The traveling driving power output device 200 outputs traveling drivingpower (torque) to driving wheel in order to cause the vehicle to travel.The traveling driving power output device 200 includes, for example, acombination of an internal combustion engine, an electric motor, and agearbox, and a power electronic control unit (ECU) configured to controlthem. The power ECU controls the configuration according to theinformation input from the second controller 160 or the informationinput from the driving operator 80.

The brake device 210 includes, for example, a brake caliper, a cylinderconfigured to transmit a hydraulic pressure to the brake caliper, anelectric motor configured to generate a hydraulic pressure in thecylinder, and a brake ECU. The brake ECU controls the electric motoraccording to the information input from the second controller 160 or theinformation input from the driving operator 80 such that a brake torqueaccording to the braking operation is output to the wheels. The brakedevice 210 may include a mechanism configured to transmit a hydraulicpressure, which is generated by an operation of the brake pedal includedin the driving operator 80 to the cylinder via the master cylinder as abackup. Further, the brake device 210 is not limited to theabove-mentioned configuration and may be an electronically controlledhydraulic brake device configured to control an actuator according tothe information input from the second controller 160 and transmit ahydraulic pressure of the master cylinder to the cylinder.

The steering device 220 includes, for example, a steering ECU and anelectric motor. The electric motor changes an orientation of a steeredwheel by, for example, applying a force to a rack and pinion mechanism.A steering ECU drives the electric motor and changes an orientation ofthe steered wheel according to the information input from the secondcontroller 160 or the information input from the driving operator 80.

[Processing Flow]

Hereinafter, a series of processing flows of the automatic drivingcontrol device 100 according to the embodiment will be described using aflowchart. FIG. 11 is a flowchart showing an example of a series ofprocessing flows of the automatic driving control device 100 accordingto the embodiment. Processing of the flowchart may be repeatedlyperformed at predetermined time intervals, for example.

First, the recognition part 130 recognizes an object present on a roadalong which the host vehicle M is traveling (step S100). The object maybe various objects such as a road marking line on the road, apedestrian, and an oncoming vehicle, as described above.

Next, the risk region calculating part 144 calculates the risk region RAon the basis of a position or a type of the road marking line, or aposition, a speed, an orientation, or the like, of another vehicletherearound (step S102).

For example, the risk region calculating part 144 divides a previouslydetermined range into a plurality of mesh squares and calculates therisk potential p for each of the plurality of mesh squares. Then, therisk region calculating part 144 calculates a vector or a tensorcorresponding to the risk potential p with respect to each mesh squareas the risk region RA. Here, the risk region calculating part 144normalizes the risk potential p.

Next, the target trajectory generating part 146 inputs the risk regionRA calculated by the risk region calculating part 144 to each of therule based models MDL1 and the DNN models MDL2 (step S104).

Next, the target trajectory generating part 146 acquires the targettrajectory TR from the rule based models MDL1 and simultaneouslyacquires the target trajectory TR from the DNN models MDL2 (step S106).Hereinafter, the target trajectory TR acquired from the rule based modelMDL1 is referred to as “a first target trajectory TR1” and the targettrajectory TR acquired from the DNN model MDL2 is referred to as “asecond target trajectory TR2.”

Next, the target trajectory generating part 146 determines whether thehost vehicle M is accelerated within a predetermined range after a speedof the host vehicle M becomes equal or smaller than a predeterminedvalue in both a first condition in which driving of the host vehicle Mis automatically controlled based on the first target trajectory TR1 anda second condition in which driving of the host vehicle M isautomatically controlled based on the second target trajectory TR2 (stepS108). The predetermined value may be, for example, 0 [km/h], or may bea speed of about several [km/h] at which the host vehicle M can beconsidered to have stopped. The predetermined range may be, for example,a range about 0 to several [G] with reference to an acceleration due togravity such that a sudden acceleration is not applied to an occupant inthe host vehicle M. That is, the target trajectory generating part 146may determine whether the host vehicle M is accelerating within a rangein which an excessive acceleration would not be applied to the occupantafter the host vehicle M temporarily stops both under the firstcondition and under the second condition.

For example, the target trajectory generating part 146 may determinewhether the host vehicle M will accelerate within the predeterminedrange after the speed of the host vehicle M has been equal to or smallerthan the predetermined value with reference to the target speed v or thetarget acceleration α included as the speed element for each of thefirst target trajectory TR1 and the second target trajectory TR2.

FIG. 12 is a view showing an example of a situation that the hostvehicle M can encounter. In a case the host vehicle M turns right at thecrossroads shown, it is necessary for the host vehicle M to travelautomatically without obstructing the progress of an oncoming vehiclem2. For example, when the host vehicle M reaches the crossroad earlierthan the oncoming vehicle m2, the automatic driving control device 100causes the host vehicle M to enter the crossroad while decelerating, andtemporarily stops the host vehicle M there until the oncoming vehicle m2passes through the crossroad. Then, the automatic driving control device100 causes the host vehicle M to turn right while accelerating while theoncoming vehicle m2 passes through the crossroad. That is, in asituation like in this crossroads, the host vehicle M can be acceleratedwithin the predetermined range after the speed of the host vehicle M hasbecome equal to or smaller than the predetermined value.

In this case, the recognition part 130 predicts a future trajectory TR #along which the recognized object will progress. For example, when therecognized object is the oncoming vehicle m2 and the turn indicatorlight of the oncoming vehicle m2 is not blinking, the recognition part130 predicts a trajectory in which the oncoming vehicle m2 will progressin a straight line as a future trajectory TR # of the oncoming vehiclem2.

Then, the target trajectory generating part 146 determines whether thefuture trajectory TR # of the oncoming vehicle m2 predicted by therecognition part 130 crosses both the first target trajectory TR1 andthe second target trajectory TR2. In a case the future trajectory TR #of the oncoming vehicle m2 crosses both the first target trajectory TR1and the second target trajectory TR2, the target trajectory generatingpart 146 determines that the host vehicle M will accelerate within thepredetermined range after the speed of the host vehicle M was equal toor smaller than the predetermined value.

Returning to description of the flowchart of FIG. 11, the targettrajectory generating part 146 selects the second target trajectory TR2from the first target trajectory TR1 and the second target trajectoryTR2 when it is determined that the host vehicle M will accelerate withinthe predetermined range after the speed of the host vehicle M has beenequal to or smaller than the predetermined value (step S110).

Meanwhile, the target trajectory generating part 146 selects the targettrajectory TR that is more optimal from the first target trajectory TR1and the second target trajectory TR2, when it is determined that thehost vehicle M will not accelerate within the predetermined range afterthe speed of the host vehicle M has been equal to or smaller than thepredetermined value (step S112).

For example, the target trajectory generating part 146 may evaluate thetarget trajectory TR from the viewpoint of smoothness of the targettrajectory TR and gentleness of the acceleration, and select the targettrajectory TR with a higher evaluation as the optimal target trajectoryTR. More specifically, the target trajectory generating part 146 mayselect the target trajectory TR with the smallest curvature x and thesmallest target acceleration α as the optimal target trajectory TR.Further, selection of the optimal target trajectory TR is not limitedthereto and may be performed in consideration of another viewpoint orthe like.

Next, the second controller 160 controls at least one of the speed andthe steering of the host vehicle M on the basis of the target trajectoryTR selected by the target trajectory generating part 146 (step S114).Accordingly, processing of the flowchart is terminated.

FIG. 13 is a view showing an example of a situation in which driving ofthe host vehicle M is controlled on the basis of the target trajectoryTR. In the situation shown, since the host vehicle M reaches thecrossroad earlier, the host vehicle M will accelerate within thepredetermined range after the speed of the host vehicle M has been equalto or greater than the predetermined value. For this reason, the secondtarget trajectory TR2 is selected instead of the first target trajectoryTR1.

As described above, the first target trajectory TR1 is the targettrajectory TR output by the rule based model MDL1, and the second targettrajectory TR2 is the target trajectory TR output by the DNN model MDL2.

It is conceivable that driving of the host vehicle M be controlled onthe basis of the first target trajectory TR1. In this case, since thefirst target trajectory TR1 is derived on the basis of the rule group,driving of the host vehicle M is controlled while following the rulesuch as the host vehicle M will turn right when the TTC between theoncoming vehicle m2 and the host vehicle M is equal to or greater than athreshold. In this case, if there are many oncoming vehicles m2, it isconceivable that the host vehicle will not be able to turn right andhave to remain in the crossroads for a long time.

Meanwhile, it is assumed that driving of the host vehicle M iscontrolled on the basis of the second target trajectory TR2. In thiscase, since the second target trajectory TR2 is derived by the CNN, theRNN, or the like, that has learned tendencies of the driving of thedriver, even in circumstances in which the host vehicle M will be stuckon a rule basis, it is expected that control of driving of the hostvehicle M would be able to be performed to overcome such circumstances.

In general, even under the circumstance in which the TTC between theoncoming vehicle m2 and the host vehicle M is less than the threshold,many drivers start turning right in the hope that the oncoming vehiclem2 will decelerate. By setting the trajectory of the vehicle when such adriver manually drives as instructor data of the DNN model MDL2, evenwhen the TTC between the oncoming vehicle m2 and the host vehicle M isless than the threshold, the DNN model MDL2 can output the second targettrajectory TR2, which causes the host vehicle M to turn right. In otherwords, it is expected that the threshold with respect to the TTC betweenthe oncoming vehicle m2 and the host vehicle M (the threshold of the TTCdetermining whether right turn is possible) would be larger in the DNNmodel MDL2 than in the rule based model MDL1.

For this reason, in a case the host vehicle M accelerates within thepredetermined range after the speed of the host vehicle M has been equalto or smaller than the predetermined value even following either one ofthe first target trajectory TR1 and the second target trajectory TR2,the second target trajectory TR2 is selected preferentially. As aresult, in a situation like a crossroads, it is possible for the hostvehicle M to be more smoothly driven automatically.

According to the above-mentioned embodiment, the automatic drivingcontrol device 100 recognizes various objects such as road marking linesor oncoming vehicles present round the host vehicle M, and calculatesthe risk region RA that is a risk region potentially present around theobject. Further, the automatic driving control device 100 inputs therisk region RA to both of the rule based models MDL1 and the DNN modelsMDL2, and generates the target trajectory TR on the basis of the outputresults of each model. The automatic driving control device 100automatically controls driving of the host vehicle M on the basis of thetarget trajectory TR. Here, the automatic driving control device 100selects the second target trajectory TR2 from the first targettrajectory TR1 and the second target trajectory TR2 when the hostvehicle M accelerates within the predetermined range after the speed ofthe host vehicle M has been equal to or smaller than the predeterminedvalue under both of the first condition in which driving of the hostvehicle M is controlled based on the first target trajectory TR1 outputby the rule based model MDL1 and the second condition in which drivingof the host vehicle M is controlled based on the second targettrajectory TR2 output by the DNN model MDL2. As a result, in a situationlike the crossroad, it is possible to more smoothly control driving ofthe host vehicle M.

<Variant of Embodiment>

Hereinafter, a variant of the above-mentioned embodiment will bedescribed. In the above-mentioned embodiment, while the targettrajectory generating part 146 has been described as inputting the riskregion RA to each of the rule based models MDL1 and the DNN models MDL2and generating the target trajectory TR on the basis of the outputresult of each model, it is not limited thereto. For example, the targettrajectory generating part 146 may input the risk region RA to a modelcreated based on a method referred to as a model based or a model baseddesign (hereinafter, referred to as a model based model), and thus, maycause the model based model to output the target trajectory TR. Themodel based model is a model of determining (or outputting) the targettrajectory TR according to the risk region RA using an optimizationmethod such as model prediction control (MPC) or the like. The modelbased model is another example of “a first model.”

In addition, the target trajectory generating part 146 may input therisk region RA to a model using another machine learning as a base, forexample, a binary tree type model, a game tree type model, a model inwhich bottom layer neural networks are coupled to each other like aBoltzmann machine, a reinforcement learning model, or a deepreinforcement learning model as a base, and thus, may cause the anothermachine learning model to output the target trajectory TRmachinelearning. The reinforcement learning is, for example, a learning methodof performing learning by repeating trial and error or optimizing anevaluation function optimized when the target trajectory TR is provided.The binary tree type model, the game tree type model, the model in whichbottom layer neural networks are coupled to each other like a Boltzmannmachine, the reinforcement learning model, the deep reinforcementlearning model, or the like, is another example of “a second model.”

[Hardware Configuration]

FIG. 14 is a view showing an example of a hardware configuration of theautomatic driving control device 100 of the embodiment. As shown, theautomatic driving control device 100 has a configuration in which acommunication controller 100-1, a CPU 100-2, a RAM 100-3 used as aworking memory, a ROM 100-4 configured to store a booting program or thelike, a storage device 100-5 such as a flash memory, an HDD, or thelike, a drive device 100-6, and the like, are connected to each other byan internal bus or a dedicated communication line. The communicationcontroller 100-1 performs communication with components other than theautomatic driving control device 100. A program 100-5 a executed by theCPU 100-2 is stored in the storage device 100-5. The program isdeveloped in the RAM 100-3 by a direct memory access (DMA) controller(not shown) or the like, and executed by the CPU 100-2. Accordingly,some or all of the first controller and the second controller 160 arerealized.

The above-mentioned embodiment can be expressed as follows.

A vehicle control device is configured to include:

at least one memory in which a program is stored; and

at least one processor,

wherein the processor executes the program to:

recognize an object present around a vehicle:

calculate a region of a risk distributed around the recognized object;

input the calculated region to each of a plurality of models thatoutputs a target trajectory along which the vehicle is to travel whenthe region is input, and generate a plurality of target trajectories onthe basis of an output result of each of the plurality of models towhich the region has been input; and

automatically control driving of the vehicle on the basis of thegenerated target trajectory,

the plurality of models include a first model which is rule based modelor model based model that output the target trajectory from the region,and a second model which is a machine learning based model learned tooutput the target trajectory when the region is input, and

the second target trajectory is selected from the first targettrajectory and the second target trajectory in a case the vehicleaccelerates after a speed of the vehicle has been equal to or smallerthan a predetermined value under a first condition and in a case thevehicle accelerates after a speed of the vehicle has been equal to orsmaller than the predetermined value under a second condition, the firstcondition being a condition in which driving of the vehicle iscontrolled based on a first target trajectory that is the targettrajectory output by the first model, the second condition being acondition in which driving of the vehicle is controlled based on asecond target trajectory that is the target trajectory output by thesecond model.

While preferred embodiments of the invention have been described andillustrated above, it should be understood that these are exemplary ofthe invention and are not to be considered as limiting. Additions,omissions, substitutions, and other modifications can be made withoutdeparting from the scope of the present invention. Accordingly, theinvention is not to be considered as being limited by the foregoingdescription, and is only limited by the scope of the appended claims.

What is claimed is:
 1. A vehicle control method, comprising: recognizingan object present around the vehicle; calculating a region of a riskdistributed around the recognized object; inputting the calculatedregion to each of a plurality of models that output a target trajectoryalong which the vehicle is to travel when the region is input, andgenerating a plurality of target trajectories on the basis of an outputresult of each of the plurality of models to which the region has beeninput; and automatically controlling driving of the vehicle on the basisof the generated target trajectory, wherein the plurality of modelsinclude a first model which is a rule based model or a model based modelthat outputs the target trajectory from the region, and a second modelwhich is a machine learning based model learned to output the targettrajectory when the region is input, and the second target trajectory isselected from the first target trajectory and the second targettrajectory in a case the vehicle accelerates after a speed of thevehicle has been equal to or smaller than a predetermined value under afirst condition and in a case the vehicle accelerates after a speed ofthe vehicle has been equal to or smaller than the predetermined valueunder a second condition, the first condition being a condition in whichdriving of the vehicle is controlled based on a first target trajectorythat is the target trajectory output by the first model, the secondcondition being a condition in which driving of the vehicle iscontrolled based on a second target trajectory that is the targettrajectory output by the second model.
 2. The vehicle control methodaccording to claim 1, further comprising: predicting a future trajectoryof the recognized object, and in a case the future trajectory of theobject crosses the target trajectory of the vehicle, a determination ismade that the vehicle will accelerate after a speed of the vehicle hasbeen equal to or smaller than the predetermined value under the firstcondition and that the vehicle will accelerate after a speed of thevehicle has been equal to or smaller than the predetermined value underthe second condition.
 3. The vehicle control method according to claim1, wherein the predetermined value is zero, and the second targettrajectory is selected among the first target trajectory and the secondtarget trajectory in a case an acceleration of the vehicle is within apredetermined range after a speed of the vehicle has been stopped underthe first condition and an acceleration of the vehicle is within apredetermined range after a speed of the vehicle has been stopped underthe second condition.
 4. A vehicle control device, comprising: arecognition part configured to recognize an object present around avehicle; a calculating part configured to calculate a region of a riskdistributed around the object recognized by the recognition part; agenerating part configured to input the region calculated by thecalculating part to each of a plurality of models that output a targettrajectory along which the vehicle is to travel when the region isinput, and generate a plurality of target trajectories on the basis ofan output result of each of the plurality of models to which the regionhas been input; and a driving controller configured to automaticallycontrol driving of the vehicle on the basis of the target trajectorygenerated by the generating part, wherein the plurality of modelsinclude a first model which is rule-based model or model-based modelthat outputs the target trajectory from the region, and a second modelwhich is a machine learning based model learned to output the targettrajectory when the region is input, and the generating part selects asecond target trajectory among a first target trajectory and a secondtarget trajectory in a case the vehicle accelerates after a speed of thevehicle has been equal to or smaller than a predetermined value under afirst condition and in a case the vehicle accelerates after a speed ofthe vehicle has been equal to or smaller than the predetermined valueunder a second condition, the first condition being a condition in whichdriving of the vehicle is controlled based on a first target trajectorythat is the target trajectory output by the first model, the secondcondition being a condition in which driving of the vehicle iscontrolled based on a second target trajectory that is the targettrajectory output by the second model.
 5. A computer-readable storagemedium on which a program is stored to execute a computer mounted on avehicle to: recognize an object present around the vehicle; calculate aregion of a risk distributed around the recognized object; input thecalculated region to each of a plurality of models that output a targettrajectory along which the vehicle is to travel when the region isinput, and generate a plurality of target trajectories on the basis ofan output result of each of the plurality of models to which the regionhas been input; and automatically control driving of the vehicle on thebasis of the generated target trajectory, wherein the plurality ofmodels include a first model which is a rule based model or a modelbased model that outputs the target trajectory from the region, and asecond model which is a machine learning based model learned to outputthe target trajectory when the region is input, and the generating partselects a second target trajectory among a first target trajectory and asecond target trajectory in a case the vehicle accelerates after a speedof the vehicle has been equal to or smaller than a predetermined valueunder a first condition and in a case the vehicle accelerates after aspeed of the vehicle has been equal to or smaller than the predeterminedvalue under a second condition, the first condition being a condition inwhich driving of the vehicle is controlled based on a first targettrajectory that is the target trajectory output by the first model, thesecond condition being a condition in which driving of the vehicle iscontrolled based on a second target trajectory that is the targettrajectory output by the second model.